1. Technical Field
The present teaching relates to methods, systems, and programming for Internet services. Particularly, the present teaching relates to methods, systems, and programming for providing information to Internet users.
2. Discussion of Technical Background
Online recommendation systems are a subclass of information filtering systems that predict an “interest” that a user would have in online content (such as articles, news, music, books, or movies), using a model built based on the characteristics of users and the content related thereto and the user's online behaviors. For example, traditional recommendation systems typically produce a list of recommendations in one of two ways—through collaborative filtering or content-based filtering. Collaborative filtering approaches build a model from a user's past behaviors (e.g., merchants previously purchased or selected, numerical ratings given to those merchants), as well as similar decisions made by other users, and use that model to predict other items that the user may be interest in. Content-based filtering approaches utilize a series of discrete characteristics of known content in order to recommend additional content with similar properties.
The prediction accuracy of the traditional recommendation systems is mainly relied on the amount of user's past behavior data that the recommendation system can obtain. For example, in order to estimate topics that a user would be interested in, traditional recommendation systems need to monitor and collect as much of the user's past online activities and related content as possible. However, if the user is new to a recommendation system, it would be very difficult for the recommendation system to obtain enough past behavior data of the new user in order to make a meaningful recommendation. Furthermore, traditional recommendation systems usually only acquire data voluntarily provided by users, e.g., through questionnaires, or data recorded by the recommendation systems when users are directly interacting with the recommendation systems, e.g., cookies or activity logs when the users are signing in the recommendation systems. As a result, inactive users of the recommendation systems cannot be used to provide data for building recommendation models. Accordingly, for new users or inactive users, the traditional recommendation systems become less effective in personalized content recommendation. In addition, traditional systems usually consider only explicit relationships among users, and interests of users explicitly expressed based on their online content consumption activities. Implicit relationships, although handled in some existing technologies, are most identified via ad hoc approaches.
Therefore, there is a need to provide an improved solution for personalized content recommendation based on information associated with users, whether such information is static, dynamic, explicit or implicit, all in a systematic and effective manner to solve the above-mentioned problems.